Skip to main content
NCBI home page
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2023 Apr 20:1–19. Online ahead of print. doi: 10.1007/s10796-023-10396-4

ICT, Gender Inequality, and Income Inequality: A Panel Data Analysis Across Countries

Christina Sanchita Shah 1, Satish Krishnan 1,✉
PMCID: PMC10115608  PMID: 37361889

Abstract

ICT has been long recognized as a driver of sustainable development goals (SDGs). This study examines the relationship between ICT, gender (in)equality (SDG 5), and income inequality (SDG 10). We conceptualize ICT as an institutional actor and use the Capabilities Approach to theorize the relationships between ICT, gender inequality and income inequality. This study uses publicly available archival data to conduct a cross-lagged panel analysis of 86 countries from 2013 to 2016. The key contributions of the study include the establishment of the relationship between (a) ICT and gender inequality and (b) gender inequality and income inequality. We also make methodological contributions to the field by employing cross-lagged panel data analysis to further our understanding of the links between ICT, gender equality, and income inequality over time. Our findings have implications for both research and practice, which are discussed.

Keywords: ICT, Gender inequality, Income inequality, Panel data

Introduction

Information and communication technologies (ICTs) have spread to every corner of the globe. Their use is pervasive and widespread (Akande et al., 2019; Walsham, 2017). People may now connect, network, and interact on a larger global scale than ever before. Over the years, major organizations such as the United Nations and the World Bank have paid close attention to the role of ICT as a catalyst for development (United Nations, 2005). It has been shown through research that ICT contributes positively to national GDP growth, and that innovative activities mediated by ICT and digitalization are crucial to national GDP expansion over the long term (Ishnazarov et al., 2021; Kurniawati, 2022; Niebel, 2018). According to the World Bank’s (2016b) report, the world is in the middle of a great ICT transformation such that mobile phones are more readily available than bathrooms or clean water. According to IS scholars, there is a pressing need to address major social issues worldwide through sustainable research (Akande et al., 2019; Bednar & Welch, 2020; Delgosha et al., 2021; Parthiban et al., 2020). ICTs promote not only economic growth but also sustainable growth. Prior research has demonstrated the connection between ICT and a variety of sustainable development goals (SDGs). Strategic use of ICTs can improve both economic and sustainable development (Henry, 2012; Rothe, 2020; Rothe et al., 2022). Women, in particular, can benefit from ICT since it has increased their access to education, health and nutrition benefits, according to research and studies. For example, WAP phones were given to women in Senegal as part of a project supported by NGOs and international organizations to let them check the price of food and connect with other women in the network (United Nations, 2005). ICT has the potential to reduce some of the barriers and obstacles faced by women. Women and girls can have more public and private agency if they have access to information and communication technologies. It has the potential to be a very useful method of reaching out to women and girls who, for a variety of reasons, are marginalised from mainstream society. Gender equality is not just a basic human right, but also a prerequisite for a peaceful, affluent, and sustainable planet. Although there has been improvement in recent decades, the world is still not on schedule to achieve gender equality by 2030. The social and economic consequences of the COVID-19 pandemic have exacerbated the issue (Sida, 2015). Prior to the pandemic, women accounted for 39.4 percent of total employment in 2019. Of the worldwide job losses in 2020, women account for roughly 45 percent (United Nations, 2022). According to the International Telecommunication Union, the percentage of women holding higher-ranking, better-paying roles is still quite low in the majority of nations. Even out of the 2.7 billion people unconnected by the internet, majority are girls and women (ITU, 2022). ICT can be formidable catalyst for gender equality of women given its availability, opportunities for capacity-building, employment, and potential for empowerment are expressly acknowledged (UN Women, 2005). Therefore, for women and girls to be able to engage in society, access to ICT is essential. However, gradually it is becoming accepted in the development world that policies and activities that do not account for gender inequality and do not address the disparity between men and women will be ineffective and expensive in the long run (Heeks, 2010). In fact, goal 5 of the Sustainable Development Goals (SDG) specifically calls for action to achieve gender parity and one of its sub-targets clearly states “promote empowerment of women through technology.” In research as well, there is a longstanding call to focus on studying ICTs and gender, as women are important contributors in many areas where ICTs are launched (Walsham, 2005, 2017). The pervasiveness, accessibility, and widespread use of ICT can be seen as having a significant impact on development (Walsham, 2017) including helping women realize gender equality. Having a well-founded ICT penetration and access can help improve gender equality in a variety of ways, laying the groundwork for gender-inclusive policy and planning. ICT can be a valuable resource for women's empowerment, particularly in the economic sphere (Verma et al., 2022).

Another issue of widespread concern is income inequality and its inclusion as sustainable development goal no. 10 (SDG 10) emphasises its importance as a global concern. The trend of rising inequality over the years in most countries is a cause for serious concern among economists and government leaders everywhere. Notwithstanding technological advancements, market-oriented reforms, and country integration, the benefits of increased incomes and output development have not been distributed fairly to all parts of the population (Asteriou et al., 2014). In addition to playing a significant role in gender equality, ICT also impacts economic growth as ICT has the potential to cut transaction costs, increase productivity and improve corporate efficiency (Evans, 2019). ICT impact a country's economic growth, which is often linked to more investments and greater employment, allowing a greater number of people to access jobs and money and thereby lowering income inequalities (Sharafat & Lehr, 2017). However, the current pattern of uneven economic growth in countries, especially developing countries, is more likely to increase income disparities. As a result, developing countries frequently suffer from an unequal distribution of income, where a sizable share of the money is typically confined to a small proportion of the population. Therefore, in an effort to minimize the income gap, countries are adopting new policies. The inclusion of income inequality as sustainable development goal no. 10 (SDG 10) emphasises its importance as a global concern. Therefore, it is anticipated that income inequality would be one of the most crucial considerations in adopting any policy in line with the 2030 SDG targets (Sinha et al., 2020).

With the significant rise in ICT penetration in countries, we motivate our study in an effort to investigate how ICT affects gender equality and income inequality over time. Although we acknowledge the contributions of previous research on ICT and income inequality (Antonelli & Gehringer, 2017; Asongu et al., 2019; Sinha et al., 2020; Tchamyou et al., 2019), there is still room for more research by adding a social perspective, such as the impact of gender equality. While many empirical studies have been undertaken on the topic of ICTs and income inequality, few have attempted to comprehensively analyse the gender element of this relationship. The study by Valberg (2020) comes close but the focus of their investigation is how ICTs affect gender equality in the labor market. Further, while many studies have explored a wide variety of impacts of ICT on women (Graf, 2020), there have been very few investigations into the direct impacts of ICT on gender (in)equality specifically. This study aims to fill this gap by conducting a cross-country investigation into the relationships between ICT, gender (in)equality and income inequality. The extent to which ICT is established and employed, as well as the degree of income inequality and gender equality, differs markedly across countries. For example, mobile and internet use is much higher in Finland, Poland, and Singapore than Tanzania, Uganda and Ethiopia (Baller et al., 2016). Similarly, gender inequality is comparatively lower in Switzerland, Denmark and Sweden than in Ghana, Bangladesh and Tanzania (UNDP, 2016). Income inequality too differs across countries from Denmark, Iceland, and Czech Republic where it is relatively lesser than Lesotho, Ghana, and Senegal (World Bank, 2016a). Thus, motivated by the observed diversity in these factors across nations, this study's primary objective is to examine the relationship between ICT, gender inequality, and income inequality at the global cross-country level and to present a multilateral interplay of the phenomena. Therefore, in this study, our goal is to understand the impact of ICT on gender equality and income inequality across countries over time. This leads us to the following research question:

  • RQ: What is the relationship between a country’s ICT, gender equality, and income inequality over time?

The key contributions of this study are as follows. First, this study proposes theoretically sound reasoning for the relationships between ICT, gender equality and income inequality. We conceptualize ICT as an institutional enabler and draw from Capabilities Approach to understand these relationships. Second, this study extends the understanding of the relationship between ICT and SDGs, such as gender equality and income inequality, by conducting a macro-level cross-lagged panel data analysis on 86 countries for 2013, 2014, 2015, and 2016. Third, we also make methodological contributions to the field by employing cross-lagged panel data analysis to further our understanding of the links between ICT, gender equality, and income inequality over time. This paper is organized as follows: section two explains the theoretical underpinnings behind the study and develops testable hypotheses. Section three explains the research methodology, section four describes the study’s results, and section five discusses the results. Concluding remarks are made at the end of the paper.

Theory and Hypotheses

Sustainable Development Goals (SDGs)

The Sustainable Development Goals (SDGs) are a list of 17 goals to make the world a better and more sustainable place. The United Nations General Assembly established the Sustainable Development Goals (SDGs) in 2015 to achieve them by 2030. They are inscribed in the “Agenda 2030”, which is a resolution by the United Nations. The SDGs were established as part of the “Post-2015 Development Agenda” to replace the “Millennium Development Goals”, which were set to expire in 2015 (United Nations, 2015b). This study focuses on SDG 10, named “Reducing Inequality”, and SDG 5, named “Gender Equality”, respectively.

SDG 10 focuses on reducing inequality among and within countries. It comprises ten sub-targets which are named “reduce income inequalities”, “promote universal social”, “economic and political inclusion”, “ensure equal opportunities and end discrimination”, “adopt fiscal and social policies that promote equality”, “improved regulation of global financial markets and institutions”, “enhanced representation for developing countries in financial institutions”, “responsible and well-managed migration policies”, “special and differential treatment for developing countries”, “encourage development assistance and investment in the least developed countries”, and “reduce transaction costs for migrant remittances”. The focus of this study is only the first sub-target, namely reducing income inequality. Reducing income inequality aims to attain sustained income growth for the bottom forty per cent of the population at a rate greater than the national average by 2030 (United Nations, 2015a).

SDG 5, on the other hand, stands for achieving gender equality and empowering all girls and women. It consists of nine sub-targets which are named “end discrimination against women and girls”, “end all violence against and exploitation of women and girls”, “eliminate forced marriages and genital mutilation”, “value unpaid care and promote shared domestic responsibilities”, “ensure full participation in leadership and decision-making”, “universal access to reproductive rights and health”, “equal rights to economic resources, property ownership and financial services”, “promote empowerment of women through technology” and “adopt and strengthen policies and enforceable legislation for gender equality”. This study focuses on the penultimate sub-target of women empowerment through technology (United Nations, 2015a).

In line with the SDGs, the Association for Information Systems (AIS) established the AIS Sustainability Task Force in June 2019 to address global sustainability challenges (AIS STF). This effort focuses on expanding on the Millennium Development Goals (MDG, 2000–2015) outcomes and using them to solve the problems connected with the UN Sustainable Development Goals (SDG, 2016–2030) (Watson et al., 2021). Additionally, the AIS Women’s Network (AISWN) College fosters a network of women researchers in IS and develops mentoring links to help women thrive (Watson et al., 2021), thereby promoting gender equality for women (SDG 5).

However, there has only been a limited focus on the SDGs in IS research thus far (Watson et al., 2021). Shifting the focus to SDGs in IS research uncovers new avenues beyond the customary areas of organizational research (Henriksen et al., 2021). In a review by Leong et al. (2020), it was found that very few IS studies are devoted to SDG 5 (gender equality) and SDG 10 (income inequality). This is surprising as there have been longstanding calls for greater emphasis on gender issues and women empowerment (Walsham et al., 2007). Further, Leong et al. (2020) also observed that IS research does not frequently participate in policy reforms and development. In most cases, the research is carried out at the micro-or meso-level, with an emphasis on the immediate organizational or community setting. As a result, there is a need for the IS field to participate in grand societal challenges (Hasan et al., 2017; Leong et al., 2020) and reflect on the bigger consequences of ICT for policymaking (Walsham et al., 2007). This study is a contribution towards this end to understand the relationship between ICT, gender equality, and income inequality at a global level.

ICT as an Institutional Actor and the Capability Approach

We conceptualize ICT as an institutional actor to understand its influence on the sustainable development goals of income inequality and gender equality. ICT is referred to as a collection of social and technical entities in its role as an institutional actor. Here, we refer to institutions as “historically developed patterns of social actions and relations, which have acquired a taken-for-granted meaning, value, and significance and are not subject to technical/rational considerations” (Avgerou, 2003). Institutions are created, maintained, and transformed through a combination of technological, cultural, and political methods (Guillén et al., 1992). ICT, therefore, as an institutional actor, includes both social and technical entities. Further, while ICT is an industry in its own right, it is also a key component of other institutions and, thus, is interrelated to other institutions. ICT is also considered to be part of social networks and is rooted in social institutions rather than just being a collection of material objects that function according to certain technical rules. It is, therefore, apparent that ICT impacts both the social and technical aspects of society. As a result, it plays a crucial role in the discussion of societal socio-economic changes today (Walsham, 2005; Webster, 2014).

ICT’s role as an institutional actor becomes especially vital when examining its influence on sustainable development goals. ICT as an institution includes the construction and diffusion of technological practices into all aspects of human life, as well as the creation of legitimate systems dedicated to maintaining such practices. Thus, we see that when ICT is conceptualized as an institutional actor deeply embedded in social, economic, and technical aspects of society, it has a twofold impact: it promotes economic growth while also broadening possibilities through access to information and knowledge (UN Women, 2005). Hence, ICT, in its institutional capacity, can act as an enabler for economic as well as social development and has the transformational potential to bring about socio-economic changes especially in developing countries (Avgerou, 2010; Marais, 2015).

Traditional welfare economic models generally link welfare to economic growth (Heeks & Arun, 2009; Walsham, 2017). In contrast, we draw from the Capabilities Approach (Sen, 1999) to submit that ICTs enable capabilities (Qureshi, 2011; Zheng, 2009). The two main components of the capability approach are "functionings" and "capabilities." "Functionings" are things that a person has already done, like being educated (Sen, 1999). On the other hand, capabilities refer to an individual's possible "being and doings" in terms of, for example, the many different lifestyles and activities that can be achieved (Chigona & Davids, 2014; Sen, 1999; Walsham, 2017). Capabilities are a person's ability to combine various functionings (Sen, 1999). In other words, functionings refer to the outcomes of a person's decisions, whereas capabilities allude to freedoms. Development in this context refers to people's rights, opportunities, and entitlements and how they can benefit economically. According to the capability approach, ICT can improve human capabilities (Bass et al., 2013) particularly in developing countries (Qureshi, 2011) as well as for those with limited resources, for example, women. This is why we regard ICT as a way of enhancing human capabilities and allowing individuals to live the lives they want (Qureshi, 2011; Zheng, 2009). The capabilities approach has been applied in several studies to explain the relationship between ICTs and development (Haenssgen & Ariana, 2018; Hatakka et al., 2014; Kleine, 2013; Oosterlaken & van den Hoven, 2011).

ICT and Gender (in)equality (SDG5)

Gender equality is a universally and historically recognized component of sustainable economic and human development and is listed as SDG 5 under the United Nation’s sustainable development goals. The United Nations has designated ICT as a socio-economic development catalyst, especially for women empowerment (United Nations, 2005). ICT is seen as a tool that could be used to increase women’s participation and contribution to economic growth (Walsham, 2017; Watson et al., 2021). ICTs have transformed the movement of information and knowledge worldwide due to their low cost of use and ability to overcome distance (Chen, 2004). ICTs can thus be used to empower women and ensure equal involvement in all sectors of society. Because ICTs allow for a greater flow of information, the growing availability and use of ICTs provides increased exposure to other cultures and communities’ customs, norms, and practices. As a result, there is a higher awareness of gender inequality concerns. Consequently, ICTs may be an effective tool for favorably changing people’s attitudes about women, including women themselves, through broadcasting educational programs on gender parity. Additionally, increased pressure from a well-informed constituency concerned about gender inequality may persuade legislators to integrate gender as a key component of their socio-economic policies (Chen, 2004).

Because of their ability to transcend physical barriers and low access costs, ICTs have transformed the global mobility of information and knowledge. Allowing women to receive education via distance learning or allowing female workers to work from home are two examples of how ICTs can help increase gender equality (Chen, 2004). Further, ICTs make trade easier by boosting the flow of information, reducing transaction costs, and eliminating barriers like distance. These gains are particularly significant for women-owned micro and small businesses. For example, self-employed women in India, Cambodia, and Peru have taken advantage of e-commerce's economic prospects (World Bank, 2003). ICTs can also enable better networking for women. For example, the “Grameen Phones Program” in Bangladesh, the “Development via Radio Program” in Zimbabwe and the use of wireless solutions for women in Bolivia and the Dominican Republic have shown that ICT can, in fact, be a tool for economic empowerment for women (United Nations, 2005). There is also evidence that cell phones, for example, may empower women in multiple ways, including by allowing them to engage in economic activities (Oreglia & Srinivasan, 2016). According to research by Ameen et al. (2021), women company owners can benefit from using smartphones and mobile apps since it gives them access to novel solutions to common problems they experience in the business world.

Drawing from the above arguments, we submit that ICT will positively impact women, thereby reducing gender inequality within a country. The more women use ICT, the better informed they become and are more likely to engage in activities that benefit themselves and everyone around them, leading to lower gender inequality. Further, we postulate that the positive impact of ICT will rise with time, decreasing a country’s gender inequality levels. Thus, we hypothesize:

  • H1a: The ICT of a country will have a negative impact on its gender inequality.

  • H1b: The effect of ICT on gender inequality increases over time.

Gender (in)equality (SDG5) and Income Inequality (SDG10)

Economic expansion is essential to every country. Countries strive towards improving their economic growth levels as a zero-growth economy is unsustainable in the long run (Gordon & Rosenthal, 2003). In fact, the growth imperative must be sufficiently high in modern capitalist economies, or enterprises will be unable to realize aggregate profits. Firms that cannot generate profits over lengthy periods of time will stop investing and eventually go out of business, resulting in a widespread economic slowdown (Binswanger, 2009; Gordon & Rosenthal, 2003). Traditionally, neoclassical growth models have emphasized external factors which do not account for technological advancement. However, in modern economies, technology has come to play a critical role in their economic growth (Gurumurthy, 2006). ICT is thus an important component of an economy’s internal development fueled by innovation and human resources (Thompson, 2008). As a result, countries that invest in telecommunications are more prepared to leverage ICT for their development (Aker & Mbiti, 2010; Thompson, 2008).

Although technology is frequently associated with an increase in employment, economic growth, and corporate and organizational innovation, institutional theorists contend that technology also impacts human views of what constitutes a resource and cultural norms (Greenwood & Holt, 2008). Adopting this view, women can be considered as untapped resources within an economy whose participation in the labour force can spur economic growth. Therefore, maximizing women’s potential is a significant issue, and various programs have cropped up worldwide to address it (Watanabe et al., 2017).

Scholars in development, gender and technology fields have extensively investigated the relationships between economic growth and technology (Cole & Mogab, 1987; Greenwood & Holt, 2008). As early as the 1970s, Boserup (1970) promoted the Women in Development (WID) method, which states that women must be included in the formal sector in order for countries to boost overall economic production. Greater gender equality would make women more economically productive, and with greater economic success, women, in turn may benefit as well. Further, economic progress is aided by gender equality in education and employment (Dorius & Firebaugh, 2010).

A key impact of ICT proliferation in the labour market is that it creates new jobs, making labour markets more inclusive and transparent. It is widely accepted that ICTs facilitate the inclusion of low-skilled and previously excluded groups, such as women, into the labour market (Raja, 2016). Gonzales et al. (2015) further indicate that pay gaps between men and women add significantly to income inequality, and greater variations in labour force participation rates between men and women lead to earnings disparities between the sexes, generating and worsening economic disparity. Women are more likely to work in the informal economy, where pay is lower, expanding the wage gap between men and women and exacerbating economic inequality.

In sum, research suggests that gender inequality is linked to income inequality. When a country’s level of gender inequality increases, women’s participation in the labour force also decreases, resulting in increased income inequality. Therefore, we submit that a higher degree of gender inequality is positively associated with increased income inequality in a country. Thus, we hypothesize:

  • H2a: Gender inequality within a country will be a positively associated with its income inequality.

  • H2b: The positive effect of gender inequality on income inequality increases over time.

Figure 1 shows the conceptual model from the above hypotheses. Incorporating four time periods, Fig. 2 shows the evolution model extended from Fig. 1. T1 in the figure refers to the first time period, T2 refers to the second time period, T3 refers to the third time period and T4 refers to the fourth time period. The primary purpose of this study is to examine the evolution of direct and indirect effects between constructs. We explain in more details in the next section.

Fig. 1.

Fig. 1

Open in a new tab

Conceptual model

Fig. 2.

Fig. 2

Open in a new tab

Extended conceptual model

Research Method

The suggested model was tested using archival data gathered from 86 nations for the years 2013, 2014, 2015, and 2016 from various reputed sources such as Network Readiness Index Reports, World Bank and United Nations (UN). We use these reports and databases because they provide us with data on ICT, gender inequality, income inequality as well as other indicators at the national level that are important to our study. The time frame and countries we selected were based on the accessibility of data for the study variables. To analyze the progression of the impacts investigated in this study, four-year panel data is sufficient (Serva et al., 2011). Since this is cross-country research, the quantity of time and effort required to complete such a study limits the collection of primary data (Frankfort-Nachmias & Nachmias, 2008). Using publicly available archival data further offers various advantages Jarvenpaa, (1991), including ease of replication (Calantone & Vickery, 2009), statistical generality (Kiecolt & Nathan, 1985), and resistance to common method bias (Woszczynski & Whitman, 2004). However, limitations in data availability result in panel attrition and dictate the geographical and temporal scope of the study.

We used PLS estimation to evaluate the model and looked at the beta coefficients' evolution and the significance level. Longitudinal investigations benefit greatly from PLS path modelling (Bagozzi & Cha, 1997; Fornell, 1985; Hair et al., 2011; Henseler et al., 2009; Wold, 2006). The primary goal of this study is to understand the direct and indirect effects of ICT on gender equality and income inequality over time. The evolution model is a suitable modelling type for panel data (Benitez et al., 2018; Roemer, 2016). We analyzed four points in time to establish the evolution effects more clearly. Multigroup studies are used to assess the significance of changes in beta coefficients across time. Changing effects (path coefficients) between components over time can be tested in several ways (Roemer, 2016). These are referred to as multigroup analyses in PLS (Henseler et al., 2009; Sarstedt et al., 2011). Our model views distinct groups as different points in time (Roemer, 2016). The non-parametric confidence set method is a good way to check for discrepancies in path coefficients (Sarstedt et al., 2011). We also examine the carry-over effects. Effects that ensue when a construct from one time period affects itself in the following time period are known as carry-over effects (Benitez et al., 2020; Roemer, 2016). Thus, a later evaluation of a construct is an update of its preceding evaluation. The bootstrapping procedure was used with 5,000 subsamples to test the significance of the path coefficients (Hair et al., 2011).

To study the relationship between ICT, gender equality and income inequality, we drew from various sources of renowned and globally accepted databases, as shown in Table 1. Consistent with previous literature (Asongu et al., 2019; Chavula, 2013; Efobi et al., 2018; Ganju et al., 2016; Khan et al., 2022; Sassi & Goaied, 2013; Stork, 2007), we employ the indicators of mobile phone subscriptions rate per 100 inhabitants, percentage of individuals using the internet and fixed broadband internet subscription rate per 100 inhabitants as indicators for ICT. We obtain these indicators from the Network Readiness Index reports. The World Economic Forum, in collaboration with INSEAD, publishes the Networked Readiness Index as part of its annual Global Information Technology Report. It aims to assess countries' readiness to capitalize on opportunities provided by information and communications technology (Baller et al., 2016). The index has evolved over time and was redesigned in 2019. The reports appear till 2016, and then there is a break, and it starts again in 2019 (Dutta & Lanvin, 2022). The revised NRI has different variables than those used till 2016, and therefore we use data only till 2016.

Table 1.

Data description

Constructs Variables Meaning Source
ICT Fixed broadband Internet subs/100 popa Fixed broadband Internet subscriptions per 100 population Network Readiness Index
Individuals using internet, % Percentage of individuals using the internet
Mobile phone subscriptions/100 popa Mobile telephone subscriptions (post-paid and pre-paid) per 100 population
Income inequality GINI Index The Gini index measures the extent of income inequality for a given population. It ranges from 0 (perfect equality) to 1 (extreme inequality) World Bank
Gender inequality Gender Inequality Index It shows the loss in potential human development due to inequality between female and male achievements It ranges from 0(total equality) to 1(total inequality) UNDP
Controls
  GDP GDP per capitaa The measure of a nation’s economic activity World Bank
  Democracy Democracy Index The index gauges a country's democracy Economist Intelligence Unit (EIU)
  Development status of country Human Development Index Measures the degree of human development in a country United Nations Development Programme (UNDP)
  Remittances Remittances Personal remittances include personal transfers and employee pay. Personal transfers are all current transfers between residents and non-residents World Bank
  Adult literacy Adult literacy rate Measures the percentage of 15 years and older people who can read and write World Bank
  Urban Population Urban Population growth rate Rate of growth of urban population World Bank
Voice and accountability Voice and accountability index It represents how much a country's residents may engage in choosing their government, as well as freedom of expression and media World Governance Indicators
Open in a new tab

aLogarithmically transformed

The dependent variable in this study is income inequality, represented by the Gini index (Ganju et al., 2016; Lee et al., 2018). The Gini coefficient is a statistical distribution meant to show the disparity in a country's income distribution. It ranges from 0 (perfect equality) to 1 (perfect inequality). Gender equality has been operationalized using the Gender Inequality Index (GII). The United Nations Development Program (UNDP) established the Gender Inequality Index (GII) in the 2010 Human Development Report to measure gender-based disadvantage in the areas of reproductive health, labour market and empowerment. This index is a composite measure that assesses the loss of accomplishment within a country as a direct result of gender inequality. Its value ranges between 0 to 1 (or 100%), with 0 denoting 0% inequality, which indicates that women and men are treated equally, and 1 denoting 100% inequality, which indicates that women and men are treated unequally.

Based on existing studies, we employed control variables to eliminate any external influence on the results. For income inequality, we controlled for remittances, GDP per capita, the literacy rate of adults and the country's development status. Past research has linked remittances, as it leads to increased income inequality because the majority of funds received from overseas end up benefitting wealthier households (Asongu et al., 2019; Bruno et al., 2012; Osabuohien & Efobi, 2014; Ssozi & Asongu, 2016; Tchamyou et al., 2019). GDP is an indicator of economic growth and is one of the key factors of income inequality (Berisha & Meszaros, 2020; Njangang et al., 2022; Richmond & Triplett, 2018). As a result, we include GDP per capita as a control variable to reflect an economy's overall macroeconomic situation. The literacy rate is a comprehensive indicator of education levels and is correlated with income (Omar & Inaba, 2020; Ucal et al., 2016). A country’s development status is taken from the Human Development Index published by UNDP.

Further, gender inequality is also controlled for in this study using democracy, urban population and voice and accountability freedom. Past research has shown an association between democracy and gender (in)equality (Beer, 2009; Cooray, 2012; Potrafke & Ursprung, 2012). The democracy index is used to assess democracy. The Economist Intelligence Unit (EIU), the research section of the Economist Group, compiles the Democracy Index. The urban population is also associated with gender equality, as rural residents are more conservative and have more inflexible attitudes toward conventional gender roles. Urbanization is therefore linked to gender equality (Chen, 2004). Past research has established a clear link between urban areas and gender equality (Anyanwu & Augustine, 2013; Susan Solomon et al., 2021). Voice and accountability index is taken from the World Bank’s Governance Indicators (Meisenberg & Woodley, 2015; Razavi, 2019).

Result and Analysis

We have used the Gini coefficient as a measure of income inequality in this study. The results of the analysis from Table 2 show that ICT significantly reduces gender inequality within a country for all time periods T1 to T4. Thus, we see a negative relationship between ICT and gender inequality lending support for H1a. We also hypothesized that gender inequality would be positively associated with income inequality. The results show that there is a positive and significant relationship between gender inequality and income inequality for all time periods T1 to T4, thus lending support to H2a.

Table 2.

Results of PLS estimation

Relationship Beta Coefficient P-value
   ICT (T1) ➔ Gender inequality (T1) -0.834 0.000
   ICT (T2) ➔ Gender inequality (T2) -0.030 0.067
   ICT (T3) ➔ Gender inequality (T3) -0.017 0.054
   ICT (T4) ➔ Gender inequality (T4) -0.021 0.088
   Gender inequality (T1) ➔ Income inequality (T1) 0.883 0.000
   Gender inequality (T2) ➔ Income inequality (T2) 0.210 0.027
   Gender inequality (T3) ➔ Income inequality (T3) 0.098 0.001
   Gender inequality (T4) ➔ Income inequality (T4) 0.141 0.004
Controls
   GDPa (T1)➔ Income inequality (T1) 0.829 0.004
   GDPa (T2)➔ Income inequality (T2) 0.183 0.134
   GDPa (T3)➔ Income inequality (T3) 0.005 0.460
   GDPa (T4)➔ Income inequality (T4) 0.020 0.384
   Literacy (T1) ➔ Income inequality (T1) -0.077 0.029
   Literacy (T2) ➔ Income inequality (T2) -0.098 0.112
   Literacy (T3) ➔ Income inequality (T3) -0.044 0.048
   Literacy (T4) ➔Income inequality (T4) -0.016 0.355
   Remittances (T1) ➔ Income inequality (T1) 0.140 0.067
   Remittances (T2) ➔ Income inequality (T2) 0.010 0.434
   Remittances (T3) ➔ Income inequality (T3) 0.024 0.090
   Remittances (T4) ➔ Income inequality (T4) 0.009 0.382
   Development status (T1) ➔ Income inequality (T1) -0.896 0.011
   Development status (T2)➔ Income inequality (T2) 0.071 0.363
   Development status (T3) ➔ Income inequality (T3) 0.145 0.033
   Development status (T4) ➔ Income inequality (T4) 0.129 0.107
   Democracy(T1) ➔ Gender inequality (T1) 0.248 0.056
   Democracy (T2) ➔ Gender inequality (T2) 0.029 0.147
   Democracy(T3) ➔ Gender inequality (T3) 0.019 0.092
   Democracy(T4) ➔ Gender inequality (T4) 0.040 0.016
   Urban population (T1) ➔ Gender inequality (T1) 0.128 0.005
   Urban population (T2) ➔ Gender inequality (T2) -0.005 0.192
   Urban population (T3) ➔ Gender inequality (T3) -0.006 0.234
   Urban population (T4) ➔ Gender inequality (T4) -0.001 0.441
   Voice and accountability (T1) ➔ Gender inequality (T1) -0.230 0.109
   Voice and accountability (T2) ➔ Gender inequality (T2) -0.040 0.154
   Voice and accountability (T3) ➔ Gender inequality (T3) -0.014 0.187
   Voice and accountability (T4) ➔ Gender inequality (T4) -0.016 0.095
Open in a new tab

aLogarithmically transformed

Further, we posited that the negative effect of ICT on gender inequality will increase over time (H1b), and the positive effect of gender inequality on income inequality will also increase over time (H2b). To test these hypotheses, we conducted multigroup analyses in PLS, which allowed us to see if the patterns in the beta coefficients involved in H1a and H2a were significant (Roemer, 2016). For our model, the different “groups” are interpreted as different points in time (Roemer, 2016). We examined whether the beta coefficients of the effect of ICT on gender inequality in times T1, T2, T3 and T4 were inside the bootstrapping confidence interval and whether this change was significant or not. Table 3 shows the changes in beta coefficients and whether the change is significant or not.

Table 3.

Changes in beta coefficients and test of significance

Type Time Effect Path coefficient Size of change Bias corrected confidence interval (CI) Comparison of path coefficient t + 1 with CI t and path coefficient t with CI t + 1 Path coefficient t + 1 inside CI t? Path coefficient t inside CI t + 1? Significant change?
Direct effect T1 ICT (T1) ➔ Gender inequality (T1) -0.834 0.804 (-0.918, -0.730) -0.030 > -0.730 No Yes
T2 ICT (T2) ➔ Gender inequality (T2) -0.030 (-0.057, -0.010) -0.834 < -0.057 No
T2 ICT (T2) ➔ Gender inequality (T2) -0.030 0.013 (-0.057, -0.010) -0.057 < -0.017 < -0.010 Yes No
T3 ICT (T3) ➔ Gender inequality (T3) -0.017 (-0.036, -0.004) -0.036 < -0.030 < -0.004 Yes
T3 ICT (T3) ➔ Gender inequality (T3) -0.017 -0.004 (-0.036, -0.004) -0.036 < -0.021 < -0.004 Yes No
T4 ICT (T4) ➔ Gender inequality (T4) -0.021 (-0.038,0.004) -0.038 < -0.017 < 0.004 Yes
T4 ICT (T4) ➔ Gender inequality (T4) -0.021 -0.813 (-0.038,0.004) -0.813 < -0.038 No Yes
T1 ICT (T1) ➔ Gender inequality (T1) -0.834 (-0.918, -0.730) -0.021 < -0.918 No
T1 Gender inequality (T1) ➔ Income Inequality (T1) 0.883 -0.673 (0.668,1.076) 0.210 < 0.668 No Yes
T2 Gender inequality (T2) ➔ Income Inequality (T2) 0.210 (0.057,0.345) 0.883 > 0.345 No
T2 Gender inequality (T2) ➔ Income Inequality (T2) 0.210 -0.112 (0.057,0.345) 0.057 < 0.098 < 0.345 Yes No
T3 Gender inequality (T3) ➔ Income Inequality (T3) 0.098 (0.067,0.139) 0.210 > 0.139 No
T3 Gender inequality (T3) ➔ Income Inequality (T3) 0.098 0.043 (0.067,0.139) 0.141 > 0.139 No No
T4 Gender inequality (T4) ➔ Income Inequality (T4) 0.141 (0.085,0.209) 0.085 < 0.098 < 0.209 Yes
T4 Gender inequality (T4) ➔ Income Inequality (T4) 0.141 0.742 (0.085,0.209) 0.883 > 0.209 No Yes
T1 Gender inequality (T1) ➔ Income Inequality (T1) 0.883 (0.668,1.076) 0.141 < 0.668 No
Carry-over effects T1/T2 ICT (T1) ➔ ICT (T2) 0.995 0.001 (0.991,0.996) 0.0991 < 0.994 < 0.996 Yes No
T2/T3 ICT (T2) ➔ ICT (T3) 0.994 (0.992,0.996) 0.992 < 0.995 < 0.996 Yes
T2/T3 ICT (T2) ➔ ICT (T3) 0.994 0.003 (0.992,0.996) 0.997 > 0.996 No Yes
T3/T4 ICT (T3) ➔ ICT (T4) 0.997 (0.996,0.998) 0.994 < 0.996 No
T1/T2 Gender inequality (T1) ➔ Gender inequality (T2) 0.965 0.965 (0.939,0.991) 0.939 < 0.989 < 0.991 Yes No
T2/T3 Gender inequality (T2) ➔ Gender inequality (T3) 0.989 (0.968,1.002) 0.965 < 0.968 No
T2/T3 Gender inequality (T2) ➔ Gender inequality (T3) 0.989 0.005 (0.968,1.002) 0.968 < 0.994 < 1.006 Yes No
T3/T4 Gender inequality (T3) ➔ Gender inequality (T4) 0.994 (0.979,1.006) 0.979 < 0.989 < 1.006 Yes
T1/T2 Income Inequality (T1) ➔ Income Inequality (T2) 0.905 0.077 (0.828,0.965) 0.982 > 0.965 No Yes
T2/T3 Income Inequality (T2) ➔ Income Inequality (T3) 0.982 (0.964,1.003) 0.905 < 0.964 No
T2/T3 Income Inequality (T2) ➔ Income Inequality (T3) 0.982 -0.023 (0.964,1.003) 0.959 < 0.964 No No
T3/T4 Income Inequality (T3) ➔ Income Inequality (T4) 0.959 (0.930,0.991) 0.930 < 0.982 < 0.991 Yes
Open in a new tab

The results indicate that the change in the path coefficients of ICT on gender equality is significant in times T1/T2 and T4/T1. However, the size of the change is decreasing for the effect of ICT on gender inequality. Thus, H1b is not supported. The change in path coefficients of gender inequality on income inequality is significant for time periods T1/T2 and T4/T1. While the size of the change is increasing for the effect of gender equality on income inequality, it is not significant for all time periods. Thus, H2b is not supported. We also analyzed the carry-over effects of ICT, gender inequality and income inequality, respectively (Benitez et al., 2020; Roemer, 2016). For income inequality, the carry-over effect is significant from T1 to T3. The carry-over effects of ICT is significant from T2 to T4. The carry-over effects of gender inequality is not significant. The standardized root mean square residual (SRMR) was used to measure overall model fit (Henseler et al., 2015). With a value of 0.074, the model showed a sufficient fit (Hu & Bentler, 1999).

We also performed a mediation analysis to examine if gender inequality mediated the relationship between ICT and income inequality. We checked for mediation across all four time periods of T1 to T4 in the model. The results are shown in Table 4. Results show that there is significant mediation across all four time periods. Thus, we can see that gender inequality mediates the relationship between ICT and income inequality.

Table 4.

Results of mediation analysis

Relationship Beta Coefficient P-value
ICT (T1) ➔ Gender inequality (T1) ➔ Income inequality (T1) -0.737 0.000
ICT (T2) ➔ Gender inequality (T2) ➔ Income inequality (T2) -0.006 0.091
ICT (T3) ➔ Gender inequality (T3) ➔ Income inequality (T3) -0.005 0.095
ICT (T4) ➔ Gender inequality (T4) ➔ Income inequality (T4) -0.004 0.099
Open in a new tab

Post Hoc Analysis

The results from Table 2 show that the development status of a country affects income inequality negatively in T1 and positively in T3. We conduct a post hoc analysis to further examine and compare how the results vary for developed and developing countries. In order to better the empirical analysis, it is also appropriate academically to comprehend the discrepancies between the outcomes for developed and developing countries (Krishnan & Lymm, 2016). As a result, we re-analyzed the model and included development status as a moderator with a larger value for developed nations and a lower value for developing countries as the dependent variable. Table 5 shows the results of the post hoc analysis and Table 6 shows the summary of the results.

Table 5.

Results of post hoc analysis

Relationship Beta Coefficient P-value
   ICT (T1) ➔ Gender inequality (T1) -0.834 0.000
   ICT (T2) ➔ Gender inequality (T2) -0.030 0.067
   ICT (T3) ➔ Gender inequality (T3) -0.017 0.054
   ICT (T4) ➔ Gender inequality (T4) -0.021 0.088
   Gender inequality (T1) ➔ Income inequality (T1) 0.823 0.000
   Gender inequality (T2) ➔ Income inequality (T2) 0.164 0.097
   Gender inequality (T3) ➔ Income inequality (T3) 0.064 0.015
   Gender inequality (T4) ➔ Income inequality (T4) 0.118 0.026
   Development status (T1) * Gender inequality (T1) ➔ Income inequality (T1) 0.133 0.179
   Development status (T2) * Gender inequality (T2) ➔ Income inequality (T2) 0.093 0.179
   Development status (T3) * Gender inequality (T3) ➔ Income inequality (T3) 0.076 0.008
   Development status (T4) * Gender inequality (T4) ➔ Income inequality (T4) 0.053 0.118
Controls
   GDPa (T1)➔ Income inequality (T1) 0.965 0.004
   GDPa (T2)➔ Income inequality (T2) 0.312 0.050
   GDPa (T3)➔ Income inequality (T3) 0.097 0.053
   GDPa (T4)➔ Income inequality (T4) 0.109 0.169
   Literacy (T1) ➔ Income inequality (T1) -0.177 0.117
   Literacy (T2) ➔ Income inequality (T2) -0.148 0.123
   Literacy (T3) ➔ Income inequality (T3) -0.087 0.006
   Literacy (T4) ➔Income inequality (T4) -0.033 0.262
   Remittances (T1) ➔ Income inequality (T1) 0.136 0.079
   Remittances (T2) ➔ Income inequality (T2) -0.012 0.414
   Remittances (T3) ➔ Income inequality (T3) 0.022 0.095
   Remittances (T4) ➔ Income inequality (T4) 0.005 0.420
   Development status (T1) ➔ Income inequality (T1) -1.050 0.012
   Development status (T2)➔ Income inequality (T2) -0.104 0.305
   Development status (T3)➔ Income inequality (T3) 0.012 0.432
   Development status (T4)➔ Income inequality (T4) 0.001 0.496
   Democracy(T1) ➔ Gender inequality (T1) 0.248 0.056
   Democracy (T2)➔ Gender inequality (T2) 0.029 0.147
   Democracy(T3)➔ Gender inequality (T3) 0.019 0.092
   Democracy(T4) ➔ Gender inequality (T4) 0.040 0.016
   Urban population (T1) ➔ Gender inequality (T1) 0.128 0.005
   Urban population (T2)➔ Gender inequality (T2) -0.005 0.192
   Urban population (T3)➔ Gender inequality (T3) -0.006 0.234
   Urban population (T4) ➔ Gender inequality (T4) -0.001 0.441
   Voice and accountability (T1) ➔ Gender inequality (T1) -0.230 0.109
   Voice and accountability (T2)➔ Gender inequality (T2) -0.040 0.154
   Voice and accountability (T3)➔ Gender inequality (T3) -0.014 0.187
   Voice and accountability (T4) ➔ Gender inequality (T4) -0.016 0.095
Open in a new tab

aLogarithmically transformed

Table 6.

Summary of results

Hypotheses Result
H1a: The ICT of a country will have a negative impact on its gender inequality Supported
H1b: The effect of ICT on gender inequality increases over time Not Supported
H2a: Gender inequality within a country will be positively associated with income inequality Supported
H2b: The positive effect of gender inequality on income inequality increases over time Not Supported
Open in a new tab

As can be seen from Table 4, there is positive and significant moderation only for T3. This indicates that for T3, the effect of gender inequality on income inequality is more prominent in developed countries than in developing countries. This is an interesting finding which implies that gender inequality is more strongly associated with income inequality in developed nations than in developing nations. This may be because developing countries still need time to strengthen their formal institutions in order for ICT to successfully influence inclusive development aspects such as gender equality and income inequality (Andres et al., 2016; Walsham, 2005). Developed countries, on the other hand, already have well-established institutions and systems and greater absorptive capacities (Niebel, 2018), resulting in greater and faster adverse impact of growing gender and income inequalities vis-à-vis developing nations.

Discussion

The purpose of the study was the examine the evolution of the impact of ICT on a country’s gender inequality and income inequality using panel data analysis of 86 countries. Understanding how ICT influences SDGs, particularly gender equality (SDG 5) and income inequality (SDG 10), is intriguing. SDGs were built upon Millennium Development Goals that expired in 2015. SDGs encompass broad and diverse goals that require fundamental changes, which are different for each country. Their objective is to protect the planet, end poverty and ensure prosperity for everyone (United Nations, 2018). Further, it is recognized that SDGs are interlinked, and one may affect another. In this context, we conceptualize ICT as an institutional actor deeply embedded in our society's socio-economic and technological elements. When ICT is conceptualized as an institutional actor, it has a dual impact: it promotes economic growth while also broadening possibilities through access to information and knowledge. Hence, ICT has the transformative ability to bring about socio-economic transformations specially in developing countries and can serve as an enabler for economic and social development in their institutional capacities. We employ the capabilities approach to understand that ICTs can contribute to development by removing barriers and enhancing capabilities. The capabilities-based approach is used to comprehend the individual capabilities manifested in the autonomy to use ICT to engage in development-related activities. It is in this direction that we study the impact of ICT (as an institutional enabler) on (in)gender equality and income inequality in countries.

Based on the literature, we hypothesized that the ICT of a country would have a negative impact on its gender inequality levels. In other words, ICT will help reduce gender inequality levels in countries. The results of the study support this hypothesis (H1a), and ICT shows a negative relationship with gender inequality for all time periods. We also hypothesized that the effect of ICT on gender inequality will increase with time. We find that while there is a significant change in T1, T2 and T4, the size of the change is decreasing. Thus, H2b is not supported.

This could be because ICT has two sides, both positive and negative when it comes to affecting socio-economic trends (Lee et al., 2018). However, despite recent advances in gender equality and women's empowerment, women's access to and usage of digital technology are still limited (Pawluczuk et al., 2021). Access to ICTs does not imply that everyone can use them in the same way or at the same frequency. Ultimately, access quality and context matter (Gray et al., 2017). In Latin America, for example, women are disproportionately more moderate internet users, whereas men are more frequent users (Gray et al., 2017). We also see that in Thailand, the way people use the internet is based on their gender and social group (Lopez-Sintas et al., 2020). The relevance of understanding the settings under which new technologies are accessed is demonstrated by the variances in the influence of the internet in various situations (Bhandari, 2019; Lopez-Sintas et al., 2020).

We also hypothesized that gender inequality would be positively associated with a country’s income inequality. According to the results, we find support for this hypothesis (H2a) as gender inequality is found to be positively related to income inequality for all time periods. We further hypothesized that the effect of gender inequality on income inequality would increase over time. We find that the change in path coefficients of gender inequality on income inequality is significant for time periods T1/T2 and T4/T1. While the size of the change is increasing for the effect of gender equality on income inequality, it is not significant for all time periods. Thus, H2b is not supported. This may be because income inequality is affected by many other global and local socio-economical geo-political aspects of a country. Furthermore, since the 1970s, income inequality has risen sharply in most of the world's developed economies. According to the UN Department of Economic and Social Affairs (DESA) World Social Report 2020, the richest 1% of the population increased their share of income between 1990 and 2015, while the bottom 40% earned less than a quarter of the income in all nations studied (UNDESA, 2020). Perhaps this is the reason our findings were not completely supported and requires additional research.

We conducted two additional analyses. First, we examined the role of gender inequality as a mediator in the relationship between ICT and income inequality. The results show that gender inequality does in fact mediate the relationship between ICT and income inequality across all time periods. We also conducted post hoc analysis to examine the role of development status of a country in the relationships between ICT, gender inequality and income inequality. We found that the effect of gender inequality on income inequality is more prominent in developed countries than in developing countries. This is an intriguing finding since it suggests that the relationship between gender disparity and income inequality is stronger in developed countries than in developing countries. This may be due to the fact that it will take developing nations some time to build up their formal institutions to the point where ICT can effectively affect inclusive development issues like gender parity and income inequality (Andres et al., 2016; Walsham, 2005). The negative effects of increasing gender and income disparities are felt more acutely and more quickly in developed countries due to their more developed institutions and systems and larger absorptive capacities (Niebel, 2018).

Theoretical Implications

This study makes several significant contributions to the knowledge base of ICT and sustainable development goals of gender equality (SDG 5) and income inequality (SDG 10). First, we conceptualized ICT as an institutional actor deeply ingrained in our society's social, economic, and technological facets that affect income inequality and gender equality. We use the capabilities approach to understand the use of ICT to promote development by enhancing capabilities, thereby acting as an institutional enabler for social as well as economic transformation, such as decreasing gender inequality and income inequality in countries. Second, our research extends the literature on ICT and sustainable development by focusing on two SDGs, namely SDG 5 (gender (in)equality) and SDG 10 (income inequality). These relationships are tested across four time periods, showing the impact of ICT on these two SDGs over time. While we acknowledge previous research that has examined ICT with gender equality and income inequality, there is a dearth of empirically supported theoretical insights. There are only a handful of studies that have engaged in macro socio-political analyses (Lin et al., 2015). Further, only a few studies have examined ICT and gender despite a longstanding call for more research (Walsham, 2017). Our study provides a theoretical underpinning for understanding the relationship between ICT, gender equality and income inequality. Third, although not explicitly hypothesized, our additional analysis on the mediation effect of gender inequality on the relationship between ICT and income inequality shows that SDGs are, in fact, interlinked and may affect each other directly or indirectly. While previous research has explored links between ICT and income inequality (Asongu et al., 2019; Grace et al., 2001; Parayil, 2005; Santos et al., 2017; Tchamyou et al., 2019), our study shows that gender inequality mediates the relationship between ICT and income inequality. Fourth, our post-hoc study shows that the connection between gender disparity and income inequality varies significantly depending on a country's level of development. It takes longer for developing countries to see the long-term impact of the association between gender inequality and income inequality, whereas developed countries may see it sooner as they have greater absorptive capacities and well-established formal institutions in place (Andres et al., 2016; Niebel, 2018; Walsham, 2005).

Implications for Policy

From a practical standpoint, our study offers several significant implications. Firstly, this study shows that ICT can act as an institutional enabler for bringing out socio-economic changes. Hence, policymakers are suggested to invest in ICT as part of digitization reforms towards sustainable development. ICT can be used to usher in socio-economic transformation and achieve sustainable development goals such as gender equality. Secondly, this study presents empirical evidence for a strong association between ICT and gender inequality, implying that ICT may be used to effect societal change, such as gender equality. Our study shows a negative relationship between ICT and gender inequality. This implies that ICT can be used as a tool to help women and thereby reduce gender disparities. Thirdly, this study suggests that gender inequality is positively associated with income inequality in a country. This implies that social change can impact economic change, and they are not, in fact, separate. Policymakers are therefore recommended to also focus on endogenous growth factors within a country, such as gender (in)equality, as it brings about social growth and can also adversely impact economic growth by increasing income disparities. Fourthly, this study shows that gender inequality can mediate the relationship between ICT and income inequality. Policy makers need to be mindful of the impact of ICT on gender and income inequality and develop measures for monitoring positive changes. Gender equality is not only useful for social reforms but also crucial for the economic development of a country. Policy makers are suggested to realize the significance of gender equality in amplifying economic growth and reducing poverty by reducing income inequality. Lastly, our study shows that the relationship between ICT, gender equality and income inequality varies according to a country’s developmental status. The effects of this nexus are realized sooner for developed countries, whereas, for developing countries, it takes longer. Policymakers are suggested to therefore understand the underlying social and economic mechanisms and frame policies specific to the needs of their country. This study offers implications for managers as well. Managers are encouraged to employ women by giving them ICT tools to work. ICT can be used to build women’s capabilities that will allow them to participate in the economy more. Women can meaningfully participate in the economy with the help of ICT.

Limitations and Future Research Directions

Three limitations should be considered while interpreting this study. First, this study employed historical data obtained from a variety of trustworthy sources to conduct an extensive cross-country analysis for which primary data collection was not feasible. Nonetheless, we believe that using secondary data is permissible for performing such a study because these reputable sources followed severe data-gathering efforts consistent with previous studies. Second, we drew on data from a variety of countries that were shared across many sources, which limited our ability to include all countries in our study. As a result, we limited the number of countries in our research to 85, which we believe is sufficient, considering that the desired sample size is 50 (Hair, 2010). Third, despite drawing ICT indicators from literature, they may not be able to fully capture all the dimensions of ICT. Finally, the study’s conclusions should be considered in the context of four-year panel data.

Future studies can explore several different areas. First, future research may explore this relationship beyond a four-year period over longer panel data to understand the exact nature of the influence of ICT on gender equality and income inequality. Second, this study focuses only on two SDGs and their inter-relationships. Future research may expand this relationship further to explore the interactions and interlinks between gender equality and income inequality. Third, the impact of several country-specific elements on the links between ICT, gender equality, and income inequality could not be investigated because this is a large-scale study. Studies on a micro-level that examine a particular region or country are more likely to offer further insights. Finally, future studies can use more dimensions of ICT to examine the linkages between ICT, gender equality and income inequality.

Concluding Remarks

Regardless of the immense ability of ICT to influence sustainable development goals, little research has been done to investigate the linkages between ICT, gender equality and income inequality in different nations. The current study fills this need by conducting a thorough empirical analysis to identify and theorize the linkages between ICT, gender equality, and income inequality over time. We theorize ICT as an institutional actor and ground our study in Capabilities Approach to understand these relationships. According to the results, we find that ICT can help reduce gender inequality and gender inequality in turn can help reduce income inequality. We also find in the study that this relationship varies with the development status of countries. We believe that this study reveals an interesting phenomenon, theoretical explanation, and empirical data verification that will contribute to the current literature and motivate additional research.

Acknowledgements

Satish Krishnan thanks the Indian Institute of Management Kozhikode’s Chair Associate Professorship for supporting this research.

Biographies

Christina Sanchita Shah

is a doctoral student in the Information Systems Area at the Indian Institute of Management (IIM) Kozhikode. Her research interests include ICT for development, digital inclusion, digital divide, digital gender divide, and related behavioral and managerial issues in the context of information systems. She has published in reputed conferences such as International Federation for Information Processing (IFIP), IADIS International Conference on Information Systems and Annual Convention of the Strategic Management Forum (SMF). She has served as a reviewer in reputed journals and conferences such as Information and Organization, IIM Kozhikode Society and Management Review, International Conference on Information Systems (ICIS), Pacific Asia Conference on Information Systems (PACIS), IFIP and Academy of Management (AOM) conference. She won the Best Reviewer Award at Academy of Management (AOM) Conference 2022 in Communications, Technology and Organization (CTO) division.

Satish Krishnan

received his Ph.D. in Information Systems from the National University of Singapore. He is the Chair Associate Professor of Information Systems at the Indian Institute of Management (IIM) Kozhikode. His research interests include IT resistance, fake news and disinformation, gender gap, e-government, e-business, virtual social networks, technostress, cyberloafing, and cyberbullying. He has published in leading journals, such as the Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Information and Management, International Journal of Information Management, Journal of Association for Information Science and Technology, International Journal of Hospitality Management, Communications of the Association for Information Systems, Computers in Human Behavior, Information Systems Frontiers, Scandinavian Journal of Information Systems, Technological Forecasting and Social Change, Journal of Retailing and Consumer Services, Human Resource Development Review, Journal of Global Information Technology Management, and e-Service Journal. He is on the editorial boards of various journals such as Internet Research, Technological Forecasting and Social Change, Information Systems Frontiers, International Journal of Information Management, and Computers in Human Behavior. He also serves at conferences such as PACIS and ICIS as Track Chair, Panel and Workshop Chair, Review Coordinator, or Associate Editor. He won the Outstanding Associate Editor Award at ICIS 2017, ICIS 2019, and ICIS 2022 and the Best Reviewer Award at PACIS 2022. Recognizing his substantial contributions to management research, the Association of Indian Management Scholars awarded him the 2022 Outstanding Young Management Researcher Award.

Appendix

Following is a list of nations used in data analysis:

Albania, Argentina, Armenia, Australia, Austria, Bangladesh, Belgium, Bolivia, Brazil, Bulgaria, Cameroon, Canada, Chile, Colombia, Costa Rica, Croatia, Cyprus, Czech Republic, Denmark, Dominican Republic, Ecuador, El Salvador, Estonia, Ethiopia, Finland, France, Georgia, Germany, Ghana, Greece, Guatemala, Honduras, Hungary, Iceland, India, Indonesia, Iran, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Latvia, Lesotho, Lithuania, Luxembourg, Malaysia, Malta, Mauritius, Mexico, Moldova, Mongolia, Montenegro, Nepal, Netherlands, New Zealand, Nicaragua, Norway, Panama, Paraguay, Peru, Philippines, Poland, Portugal, Romania, Russian Federation, Rwanada, Senegal, Singapore, Slovenia, Spain, Sri Lanka, Sweden, Switzerland, Tanzania, Thailand, Tunisia, Turkey, Uganda, United Kingdom, United States, Uruguay, Vietnam
Open in a new tab

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Christina Sanchita Shah, Email: christinas12fpm@iimk.ac.in.

Satish Krishnan, Email: satishk@iimk.ac.in.

References

  1. Akande A, Cabral P, Casteleyn S. Assessing the gap between technology and the environmental sustainability of European cities. Information Systems Frontiers. 2019;21(3):581–604. doi: 10.1007/s10796-019-09903-3. [DOI] [Google Scholar]
  2. Aker JC, Mbiti IM. Mobile phones and economic development in Africa. Journal of Economic Perspectives. 2010;24(3):207–232. doi: 10.1257/jep.24.3.207. [DOI] [Google Scholar]
  3. Ameen N, Madichie NO, Anand A. Between handholding and hand-held devices: Marketing through smartphone innovation and women’s entrepreneurship in post conflict economies in times of crisis. Information Systems Frontiers. 2021 doi: 10.1007/s10796-021-10198-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Andres, A. R., Amavilah, V. H. S., & Asongu, S. A. (2016). Linkages between formal institutions, ICT adoption and inclusive human development in Sub Saharan Africa. SSRN Electronic Journal. 10.2139/ssrn.2822104
  5. Antonelli C, Gehringer A. Technological change, rent and income inequalities: A Schumpeterian approach. Technological Forecasting and Social Change. 2017;115:85–98. doi: 10.1016/j.techfore.2016.09.023. [DOI] [Google Scholar]
  6. Anyanwu JC, Augustine D. Gender Equality in employment in Africa: Empirical analysis and policy implications. African Development Review. 2013;25(4):400–420. doi: 10.1111/1467-8268.12038. [DOI] [Google Scholar]
  7. Asongu SA, Orim S-MI, Nting RT. Inequality, information technology and inclusive education in sub-Saharan Africa. Technological Forecasting and Social Change. 2019;146:380–389. doi: 10.1016/j.techfore.2019.06.006. [DOI] [Google Scholar]
  8. Asteriou D, Dimelis S, Moudatsou A. Globalization and income inequality: A panel data econometric approach for the EU27 countries. Economic Modelling. 2014;36:592–599. doi: 10.1016/j.econmod.2013.09.051. [DOI] [Google Scholar]
  9. Avgerou, C. (2003). IT as an Institutional Actor in Developing Countries. The Digital Challenge: Information Technology in the Development Context, 46–62.
  10. Avgerou C. Discourses on ICT and development. Information Technologies and International Development. 2010;6(3):1–18. [Google Scholar]
  11. Bagozzi, R. P., & Cha, J. (1997). Partial least squares. In Advanced methods of marketing research (1. publ., Repr, pp. 52–78). Blackwell.
  12. Baller, S., Dutta, S., & Lanvin, B. (2016). The Global Information Technology Report 2016: Innovating in the Digital Economy. https://www.deslibris.ca/ID/10090686. Accessed 02 Apr 2022
  13. Bass, J. M., Nicholson, B., & Subrahmanian, E. (2013). A framework using institutional analysis and the capability approach in ICT4D. Information Technologies and International Development,9(1), 19–35.
  14. Bednar PM, Welch C. Socio-technical perspectives on smart working: Creating meaningful and sustainable systems. Information Systems Frontiers. 2020;22(2):281–298. doi: 10.1007/s10796-019-09921-1. [DOI] [Google Scholar]
  15. Beer C. Democracy and gender equality. Studies in Comparative International Development. 2009;44(3):212–227. doi: 10.1007/s12116-009-9043-2. [DOI] [Google Scholar]
  16. Benitez J, Chen Y, Teo TSH, Ajamieh A. Evolution of the impact of e-business technology on operational competence and firm profitability: A panel data investigation. Information & Management. 2018;55(1):120–130. doi: 10.1016/j.im.2017.08.002. [DOI] [Google Scholar]
  17. Benitez J, Henseler J, Castillo A, Schuberth F. How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management. 2020;57(2):103168. doi: 10.1016/j.im.2019.05.003. [DOI] [Google Scholar]
  18. Berisha E, Meszaros J. Macroeconomic determinants of wealth inequality dynamics. Economic Modelling. 2020;89:153–165. doi: 10.1016/j.econmod.2019.10.001. [DOI] [Google Scholar]
  19. Bhandari A. Gender inequality in mobile technology access: The role of economic and social development. Information, Communication & Society. 2019;22(5):678–694. doi: 10.1080/1369118X.2018.1563206. [DOI] [Google Scholar]
  20. Binswanger M. Is there a growth imperative in capitalist economies? A circular flow perspective. Journal of Post Keynesian Economics. 2009;31(4):707–727. doi: 10.2753/PKE0160-3477310410. [DOI] [Google Scholar]
  21. Boserup E. Woman’s role in economic development. George Allen & Unwin; 1970. [Google Scholar]
  22. Bruno G, De Bonis R, Silvestrini A. Do financial systems converge? New evidence from financial assets in OECD countries. Journal of Comparative Economics. 2012;40(1):141–155. doi: 10.1016/j.jce.2011.09.003. [DOI] [Google Scholar]
  23. Calantone R, Vickery SK. Special topic forum on using archival and secondary data sources in supply chain management research. Journal of Supply Chain Management. 2009;45(2):94–95. doi: 10.1111/j.1745-493X.2009.03165.x. [DOI] [Google Scholar]
  24. Chavula HK. Telecommunications development and economic growth in Africa. Information Technology for Development. 2013;19(1):5–23. doi: 10.1080/02681102.2012.694794. [DOI] [Google Scholar]
  25. Chen, D. (2004). Gender Equality and Economic Development: The Role for Information and Communication Technologies. World Bank.
  26. Chigona, W., & Davids, Z. (2014). Educators’ motivation on integration of ICTs into pedagogy: Case of disadvantaged areas. South African Journal of Education, 34(3), 1–8. 10.15700/201409161051
  27. Cole WE, Mogab JW. The transfer of soft technologies to less-developed countries: Some implications for the technology/ceremony dichotomy. Journal of Economic Issues. 1987;21(1):309–320. doi: 10.1080/00213624.1987.11504611. [DOI] [Google Scholar]
  28. Cooray A. Suffrage, democracy and gender equality in education. Oxford Development Studies. 2012;40(1):21–47. doi: 10.1080/13600818.2011.646976. [DOI] [Google Scholar]
  29. Delgosha MS, Saheb T, Hajiheydari N. Modelling the asymmetrical relationships between digitalisation and sustainable competitiveness: A cross-country configurational analysis. Information Systems Frontiers. 2021;23(5):1317–1337. doi: 10.1007/s10796-020-10029-0. [DOI] [Google Scholar]
  30. Dorius SF, Firebaugh G. Trends in global gender inequality. Social Forces. 2010;88(5):1941–1968. doi: 10.1353/sof.2010.0040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Dutta, S., & Lanvin, B. (2022). The Network Readiness Index 2019: Towards a Future-Ready Society. Portulans Institute. https://networkreadinessindex.org/. Accessed 02 Apr 2022
  32. Efobi UR, Tanankem BV, Asongu SA. Female economic participation with information and communication technology advancement: Evidence from Sub-Saharan Africa. South African Journal of Economics. 2018;86(2):231–246. doi: 10.1111/saje.12194. [DOI] [Google Scholar]
  33. Evans O. Repositioning for increased digital dividends: Internet usage and economic well-being in Sub-Saharan Africa. Journal of Global Information Technology Management. 2019;22(1):47–70. doi: 10.1080/1097198X.2019.1567218. [DOI] [Google Scholar]
  34. Fornell, C. (1985). A Second Generation of Multivariate Analysis: Classification of Methods and Implications for Marketing Research. The University of Michigan. https://deepblue.lib.umich.edu/bitstream/handle/2027.42/35621/b1408124.0001.001.pdf?sequence=2. Accessed 03 Apr 2022
  35. Frankfort-Nachmias C, Nachmias D. Research methods in the social sciences. 7. Worth Publishers; 2008. [Google Scholar]
  36. Ganju KK, Pavlou PA, Banker RD. Does information and communication technology lead to the well-being of nations? A country-level empirical investigation. MIS Quarterly. 2016;40(2):417–430. doi: 10.25300/MISQ/2016/40.2.07. [DOI] [Google Scholar]
  37. Gonzales C, Jain-Chandra S, Kochhar K, Newiak M, Zeinullayev T. Catalyst for change: Empowering women and tackling income inequality. Staff Discussion Notes. 2015;15(20):1. doi: 10.5089/9781513533384.006. [DOI] [Google Scholar]
  38. Gordon MJ, Rosenthal JS. Capitalism’s growth imperative. Cambridge Journal of Economics. 2003;27(1):25–48. doi: 10.1093/cje/27.1.25. [DOI] [Google Scholar]
  39. Grace, J., Kenny, C., Zhen-Wei Qiang, C., Liu, J., & Reynolds, T. (2001). Information and Communication Technologies and Broad-Based Development: A Partial Review of the Evidence. World Bank Working Paper, 12. https://openknowledge.worldbank.org/handle/10986/15053. Accessed 03 Apr 2022
  40. Graf, V. (2020). Inclusiveness In a Digitizing World – Investigating ICT And Women’s Empowerment. Research Papers, 114. https://aisel.aisnet.org/ecis2020_rp/114. Accessed 25 Mar 2023
  41. Gray TJ, Gainous J, Wagner KM. Gender and the digital divide in Latin America: Gender and the digital divide in Latin America. Social Science Quarterly. 2017;98(1):326–340. doi: 10.1111/ssqu.12270. [DOI] [Google Scholar]
  42. Greenwood DT, Holt RPF. Institutional and ecological economics: The role of technology and institutions in economic development. Journal of Economic Issues. 2008;42(2):445–452. doi: 10.1080/00213624.2008.11507153. [DOI] [Google Scholar]
  43. Guillén MF, Powell WW, Dimaggio PJ. The new institutionalism in organizational analysis. Reis. 1992;59:344. doi: 10.2307/40183831. [DOI] [Google Scholar]
  44. Gurumurthy A. Promoting gender equality? Some development-related uses of ICTs by women. Development in Practice. 2006;16(6):611–616. doi: 10.1080/09614520600958298. [DOI] [Google Scholar]
  45. Haenssgen MJ, Ariana P. The place of technology in the Capability Approach. Oxford Development Studies. 2018;46(1):98–112. doi: 10.1080/13600818.2017.1325456. [DOI] [Google Scholar]
  46. Hair JF, editor. Multivariate data analysis. 7. Prentice Hall; 2010. [Google Scholar]
  47. Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice. 2011;19(2):139–152. doi: 10.2753/MTP1069-6679190202. [DOI] [Google Scholar]
  48. Hasan H, Smith S, Finnegan P. An activity theoretic analysis of the mediating role of information systems in tackling climate change adaptation: Tackling climate change adaptation. Information Systems Journal. 2017;27(3):271–308. doi: 10.1111/isj.12104. [DOI] [Google Scholar]
  49. Hatakka M, Ater S, Obura D, Mibei B. Capability outcomes from educational and ICT Capability inputs—an analysis of ICT use in informal education in Kenya. The Electronic Journal of Information Systems in Developing Countries. 2014;61(1):1–17. doi: 10.1002/j.1681-4835.2014.tb00430.x. [DOI] [Google Scholar]
  50. Heeks R. Do information and communication technologies (ICTs) contribute to development? Journal of International Development. 2010;22(5):625–640. doi: 10.1002/jid.1716. [DOI] [Google Scholar]
  51. Heeks R, Arun S. Social outsourcing as a development tool: The impact of outsourcing IT services to women’s social enterprises in Kerala. Journal of International Development. 2009 doi: 10.1002/jid.1580. [DOI] [Google Scholar]
  52. Henriksen HZ, Thapa D, Elbanna A. Sustainable development goals in IS research. Scandinavian Journal of Information Systems. 2021;33(2):97–102. [Google Scholar]
  53. Henry BC. ICT for sustainable development. Science and Technology. 2012;2(5):142–145. doi: 10.5923/j.scit.20120205.06. [DOI] [Google Scholar]
  54. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science. 2015;43(1):115–135. doi: 10.1007/s11747-014-0403-8. [DOI] [Google Scholar]
  55. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), Advances in International Marketing (vol. 20, pp. 277–319). Emerald Group Publishing Limited. 10.1108/S1474-7979(2009)0000020014
  56. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal. 1999;6(1):1–55. doi: 10.1080/10705519909540118. [DOI] [Google Scholar]
  57. Ishnazarov, A., Kasimova, N., Tosheva, S., & Isaeva, A. (2021). ICT and Economic Growth: Evidence from Cross-Country Growth Modeling. The 5th International Conference on Future Networks & Distributed Systems, 668–671. 10.1145/3508072.3508204
  58. ITU. (2022). Bridging the gender divide. https://www.itu.int/en/mediacentre/backgrounders/Pages/bridging-the-gender-divide.aspx. Accessed 17 Aug 2022
  59. Khan A, Krishnan S, Arayankalam J. The role of ICT laws and national culture in determining ICT diffusion and well-being: A cross-country examination. Information Systems Frontiers. 2022;24(2):415–440. doi: 10.1007/s10796-020-10039-y. [DOI] [Google Scholar]
  60. Kiecolt KJ, Nathan LE. Secondary analysis of survey data. Sage Publications; 1985. [Google Scholar]
  61. Kleine D. Technologies of choice? ICTs, development, and the capabilities approach. MIT Press; 2013. [Google Scholar]
  62. Krishnan S, Lymm J. Determinants of virtual social networks diffusion: Insights from cross-country data. Computers in Human Behavior. 2016;54:691–700. doi: 10.1016/j.chb.2015.07.055. [DOI] [Google Scholar]
  63. Kurniawati MA. Analysis of the impact of information communication technology on economic growth: Empirical evidence from Asian countries. Journal of Asian Business and Economic Studies. 2022;29(1):2–18. doi: 10.1108/JABES-07-2020-0082. [DOI] [Google Scholar]
  64. Lee G, Shao B, Vinze A. The role of ICT as a double-edged sword in fostering societal transformations. Journal of the Association for Information Systems. 2018;19(3):209–246. doi: 10.17705/1jais.00490. [DOI] [Google Scholar]
  65. Leong, C., Tan, F., & Ahuja, M. (2020). IS for Good – 10 years to SDG: Where we have been and where we need to go? ICIS 2020 Proceedings. e International Conference on Information Systems (ICIS).
  66. Lin C, Kuo F-Y, Myers MD. Extending ICT4D studies: The value of critical research. MIS Quarterly. 2015;39(3):697–712. doi: 10.25300/MISQ/2015/39.3.09. [DOI] [Google Scholar]
  67. Lopez-Sintas J, Lamberti G, Sukphan J. The social structuring of the digital gap in a developing country. The impact of computer and internet access opportunities on internet use in Thailand. Technology in Society. 2020;63:101433. doi: 10.1016/j.techsoc.2020.101433. [DOI] [Google Scholar]
  68. Marais, M. (2015). ICT4D and Sustainability. In The international encyclopedia of digital communication and society (IEDCS) (1st ed.). John Wiley & Sons, Inc.
  69. Meisenberg G, Woodley MA. Gender differences in subjective well-being and their relationships with gender equality. Journal of Happiness Studies. 2015;16(6):1539–1555. doi: 10.1007/s10902-014-9577-5. [DOI] [Google Scholar]
  70. Niebel T. ICT and economic growth – comparing developing, emerging and developed countries. World Development. 2018;104:197–211. doi: 10.1016/j.worlddev.2017.11.024. [DOI] [Google Scholar]
  71. Njangang H, Beleck A, Tadadjeu S, Kamguia B. Do ICTs drive wealth inequality? Evidence from a dynamic panel analysis. Telecommunications Policy. 2022;46(2):102246. doi: 10.1016/j.telpol.2021.102246. [DOI] [Google Scholar]
  72. Omar MA, Inaba K. Does financial inclusion reduce poverty and income inequality in developing countries? A panel data analysis. Journal of Economic Structures. 2020;9(1):37. doi: 10.1186/s40008-020-00214-4. [DOI] [Google Scholar]
  73. Oosterlaken I, van den Hoven J. Editorial: ICT and the capability approach. Ethics and Information Technology. 2011;13(2):65–67. doi: 10.1007/s10676-011-9270-x. [DOI] [Google Scholar]
  74. Oreglia E, Srinivasan J. ICT, intermediaries, and the transformation of gendered power structures. MIS Quarterly. 2016;40(2):501–510. doi: 10.25300/MISQ/2016/40.2.13. [DOI] [Google Scholar]
  75. Osabuohien, S. E., & Efobi, R. U. (2014). Africa’s Money in Africa: Human and Physical Capital Dimensions. In S. Sahoo & B. K. Pattanaik (Eds.), Global Diasporas and Development (pp. 87–104). Springer India. 10.1007/978-81-322-1047-4_5
  76. Parayil G. The digital divide and increasing returns: Contradictions of informational capitalism. The Information Society. 2005;21(1):41–51. doi: 10.1080/01972240590895900. [DOI] [Google Scholar]
  77. Parthiban R, Qureshi I, Bandyopadhyay S, Bhatt B, Jaikumar S. Leveraging ICT to overcome complementary institutional voids: Insights from institutional work by a social enterprise to help marginalized. Information Systems Frontiers. 2020;22(3):633–653. doi: 10.1007/s10796-020-09991-6. [DOI] [Google Scholar]
  78. Pawluczuk A, Lee J, Gamundani AM. Bridging the gender digital divide: An analysis of existing guidance for gender digital inclusion programmes’ evaluations. Digital Policy, Regulation and Governance. 2021;23(3):287–299. doi: 10.1108/DPRG-11-2020-0158. [DOI] [Google Scholar]
  79. Potrafke N, Ursprung HW. Globalization and gender equality in the course of development. European Journal of Political Economy. 2012;28(4):399–413. doi: 10.1016/j.ejpoleco.2012.04.001. [DOI] [Google Scholar]
  80. Qureshi S. Information technology for development in expanding capabilities. Information Technology for Development. 2011;17(2):91–94. doi: 10.1080/02681102.2011.569355. [DOI] [Google Scholar]
  81. Raja DS. Bridging the disability divide through digital technologies. Digital Dividends; 2016. [Google Scholar]
  82. Razavi S. Indicators as substitute for policy contestation and accountability? Some reflections on the 2030 Agenda from the Perspective of Gender Equality and Women’s Rights. Global Policy. 2019;10(S1):149–152. doi: 10.1111/1758-5899.12633. [DOI] [Google Scholar]
  83. Richmond K, Triplett RE. ICT and income inequality: A cross-national perspective. International Review of Applied Economics. 2018;32(2):195–214. doi: 10.1080/02692171.2017.1338677. [DOI] [Google Scholar]
  84. Roemer E. A tutorial on the use of PLS path modeling in longitudinal studies. Industrial Management & Data Systems. 2016;116(9):1901–1921. doi: 10.1108/IMDS-07-2015-0317. [DOI] [Google Scholar]
  85. Rothe F-F. Rethinking positive and negative impacts of ‘ICT for development’ through the holistic lens of the sustainable development goals. Information Technology for Development. 2020;26(4):653–669. doi: 10.1080/02681102.2020.1756728. [DOI] [Google Scholar]
  86. Rothe F-F, Van Audenhove L, Loisen J. ICT for development and the novel principles of the Sustainable Development Goals. Third World Quarterly. 2022;43(6):1495–1514. doi: 10.1080/01436597.2022.2060202. [DOI] [Google Scholar]
  87. Santos M, Sequeira TN, Ferreira-Lopes A. Income inequality and technological adoption. Journal of Economic Issues. 2017;51(4):979–1000. doi: 10.1080/00213624.2017.1391582. [DOI] [Google Scholar]
  88. Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). Multigroup Analysis in Partial Least Squares (PLS) Path Modeling: Alternative Methods and Empirical Results. In M. Sarstedt, M. Schwaiger, & C. R. Taylor (Eds.), Advances in International Marketing (vol. 22, pp. 195–218). Emerald Group Publishing Limited. 10.1108/S1474-7979(2011)0000022012
  89. Sassi S, Goaied M. Financial development, ICT diffusion and economic growth: Lessons from MENA region. Telecommunications Policy. 2013;37(4–5):252–261. doi: 10.1016/j.telpol.2012.12.004. [DOI] [Google Scholar]
  90. Sen, A. (1999). Commodities and capabilities (13th impr). Oxford Univ. Press.
  91. Serva, M. A., Kher, H., & Laurenceau, J.-P. (2011). Using latent growth modeling to understand longitudinal effects in MIS theory: A primer. Communications of the Association for Information Systems, 28. 10.17705/1CAIS.02814
  92. Sharafat, A. R., & Lehr, W. (Eds.). (2017). ICT-centric economic growth, innovation and job creation 2017. ITU.
  93. Sida. (2015). Gender and ICT. Swedish International Development Cooperation Agency. https://cdn.sida.se/publications/files/-gender-and-ict.pdf. Accessed 18 Mar 2023
  94. Sinha A, Sengupta T, Kalugina O, Gulzar MA. Does distribution of energy innovation impact distribution of income: A quantile-based SDG modeling approach. Technological Forecasting and Social Change. 2020;160:120224. doi: 10.1016/j.techfore.2020.120224. [DOI] [Google Scholar]
  95. Ssozi J, Asongu SA. The effects of remittances on output per worker in Sub-Saharan Africa: A production function approach. South African Journal of Economics. 2016;84(3):400–421. doi: 10.1111/saje.12100. [DOI] [Google Scholar]
  96. Stork, C. (2007). Sustainable development and ICT indicators. International Institute for Sustainable Development.
  97. Susan Solomon D, Singh C, Islam F. Examining the outcomes of urban adaptation interventions on gender equality using SDG 5. Climate and Development. 2021;13(9):830–841. doi: 10.1080/17565529.2021.1939643. [DOI] [Google Scholar]
  98. Tchamyou VS, Erreygers G, Cassimon D. Inequality, ICT and financial access in Africa. Technological Forecasting and Social Change. 2019;139:169–184. doi: 10.1016/j.techfore.2018.11.004. [DOI] [Google Scholar]
  99. Thompson M. Ict and development studies: Towards development 2.0. Journal of International Development. 2008;20(6):821–835. doi: 10.1002/jid.1498. [DOI] [Google Scholar]
  100. Ucal M, Haug AA, Bilgin MH. Income inequality and FDI: Evidence with Turkish data. Applied Economics. 2016;48(11):1030–1045. doi: 10.1080/00036846.2015.1093081. [DOI] [Google Scholar]
  101. UN Women. (2005). Gender equality and empowerment of women through ICT.
  102. UNDESA . Inequality in a rapidly changing world. United Nations Department of Economic and Social Affairs; 2020. [Google Scholar]
  103. UNDP . Human development report 2016: Human development for everyone. United Nations Publications; 2016. [Google Scholar]
  104. United Nations. (2005). Gender Equality and Empowerment of Women through ICT. Women 2000 and beyond. United Nations.
  105. United Nations. (2015a). Transforming Our World: The 2030 Agenda for Sustainable Development. United Nations. https://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E. Accessed 12 Apr 2022
  106. United Nations. (2015b). Transforming our world: The 2030 Agenda for Sustainable Development (A/RES/70/1).
  107. United Nations. (2018). Sustainable development goals. http://www.un.org/sustainabledevelopment/sustainable-development-goals/. Accessed 18 Mar 2023
  108. United Nations. (2022). Goal 5: Achieve gender equality and empower all women and girls. Sustainable Development Goals. https://www.un.org/sustainabledevelopment/gender-equality/. Accessed 18 Mar 2023
  109. UN Women. (2005). Gender equality and empowerment of women through ICT.
  110. Valberg, S. (2020). ICT, Gender, and the Labor Market: A Cross-Country Analysis. In D. Maiti, F. Castellacci, & A. Melchior (Eds.), Digitalisation and Development (pp. 375–405). Springer Singapore. 10.1007/978-981-13-9996-1_15
  111. Verma A, Giri AK, Debata B. ICT diffusion, women empowerment, and sustainable development in SAARC countries. Journal of Economic and Administrative Sciences. 2022 doi: 10.1108/JEAS-03-2022-0073. [DOI] [Google Scholar]
  112. Walsham G. Development, global futures and IS research: A polemic. The Journal of Strategic Information Systems. 2005;14(1):5–15. doi: 10.1016/j.jsis.2004.11.009. [DOI] [Google Scholar]
  113. Walsham G. ICT4D research: Reflections on history and future agenda. Information Technology for Development. 2017;23(1):18–41. doi: 10.1080/02681102.2016.1246406. [DOI] [Google Scholar]
  114. Walsham, Robey, Sahay Foreword: Special issue on information systems in developing countries. MIS Quarterly. 2007;31(2):317. doi: 10.2307/25148793. [DOI] [Google Scholar]
  115. Watanabe C, Naveed K, Neittaanmäki P. ICT-driven disruptive innovation nurtures un-captured GDP – Harnessing women’s potential as untapped resources. Technology in Society. 2017;51:81–101. doi: 10.1016/j.techsoc.2017.07.007. [DOI] [Google Scholar]
  116. Watson RT, Elliot S, Corbett J, Farkas D, Feizabadi A, Gupta A, Iyer L, Sen S, Sharda R, Shin N, Thapa D, Webster J. How the AIS can improve its contributions to the UN’s sustainability development goals: Towards a framework for scaling collaborations and evaluating impact. Communications of the Association for Information Systems. 2021;48(1):476–502. doi: 10.17705/1CAIS.04841. [DOI] [Google Scholar]
  117. Webster, F. (2014). Theories of the information society (4th ed.). Routledge.
  118. Wold, H. (2006). Partial Least Squares. In S. Kotz, C. B. Read, N. Balakrishnan, B. Vidakovic, & N. L. Johnson (Eds.), Encyclopedia of Statistical Sciences (p. ess1914.pub2). Wiley. 10.1002/0471667196.ess1914.pub2
  119. World Bank. (2003). World Development Report 2003: Sustainable Development in a Dynamic World—Transforming Institutions, Growth, and Quality of Life. https://openknowledge.worldbank.org/handle/10986/5985. Accessed 12 Apr 2022
  120. World Bank. (2016a). Gini Index. Gini Index. https://data.worldbank.org/indicator/SI.POV.GINI. Accessed 12 May 2022
  121. World Bank. (2016b). World development report 2016b: Digital dividends. World Bank.
  122. Woszczynski A, Whitman M. The Handbook of Information Systems Research. IGI Global; 2004. [Google Scholar]
  123. Zheng Y. Different spaces for e-development: What can we learn from the capability approach? Information Technology for Development. 2009;15(2):66–82. doi: 10.1002/itdj.20115. [DOI] [Google Scholar]

Articles from Information Systems Frontiers are provided here courtesy of Nature Publishing Group

RESOURCES